Abstract Circulating miRNAs have proposed as specific biomarkers of disease states, including some of the most prevailing ones such as cardiovascular diseases and cancer. However using miRNAs as biomarkers is very challenging despite recent advances in high-throughput miRNA profiling. Various detection technologies, protocols, ligation and extraction/purification methods have led to varying miRNA profiling results of cells and biofluids under different conditions. Most importantly, all require days long sample-to-answer assay times, thus ruling them out for detection and monitoring of urgent, life threatening conditions such as myocardial infarction (MI). A rapid real-time, PCR-free miRNA-profiling device would be exceedingly valuable for precision, personalized medicine in years to come. However, it is very difficult to start even developing such a platform because of the limitations in testing models. Animal models often fail to predict responses in humans; and studies of human subjects do not readily allow for precise control over the disease events or temporal correlation of the disease state and biomarker expression dynamics. To address this challenge, in this study, we will develop an organ-on-a-chip device with an integrated attomolar (aM)-level miRNA sensing capability, which we will use for optimizing real-time monitoring of fluctuations in multiple miRNAs for novel biomarker discovery. As an immediate application, we will start with a human myocardium-on-chip (MoC) as a clinically relevant model and imitate the course of a heart attack. We hypothesize that using the MoC with ultrasensitive miRNA detection, we will discover a unique signature that indicates the onset of reperfusion injury during MI treatment. Finally, we will test the sensor device and the miRNA signature using clinical blood samples. Our microfluidic organ-on-a-chip platform will consist of four basic components: 1) the tissue engineered human MoC from human induced pluripotent stem cells (hiPSCs), 2) the exosome lysing unit, 3) the concentration unit for the lysed RNAs and 4) the detection unit for the miRNAs. In Aim 1, we will couple these components into a fully integrated microfluidic platform. First we will validate the clinical relevance of the MoC model by comparing with human tissue and blood samples. Then we will characterize and optimize the performance of a novel miRNA detection biosensor using MoC and benchmark it against established miRNA analysis techniques. In Aim 2 we will focus on multiplexing the sensing approach for the real-time detection of a panel of miRNAs, and 1) use the MoC to discover a miRNA signature to be used as a novel biomarker that captures the RI onset, as well as 2) to optimize the multiplexed sensor for faster clinical translation. In Aim 3 we will determine the diagnostic and prognostic capabilities of the novel biosensor and miRNA biomarker signature we developed in Aims 1 and 2 using the MoC model, with clinical samples from MI patients. Our long-term goal is to utilize this integrated platform to study exosomes and their RNA content to advance current understanding of their role in human health and to determine their potential as biomarkers for disease states.